Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results

Abstract

1Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-2 occurring mental processes, including perceptual and motor functions. As such, there is limited ecological 3 validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of 4 interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to 5 study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may 6 answer broader questions regarding how naturalistic cognition is biologically distributed throughout the 7 brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify

Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

“…Methodological considerations. Our clustering results may be influenced by characteristics of the k-means clustering algorithm, specifically its sensitivity to spherical clusters and its assumption that input data are linearly separable (Bottenhorn et al, 2019). Further, while this approach is, for the most part, data-driven, it requires specification of certain parameters.…”

“…Specifically, we chose 1,000 as the number of iterations performed to ensure that each solution minimized the point-to-centroid distance. This decision was made based on prior work (Bottenhorn et al, 2019;Kanungo, 2004). We also selected the additive inverse of the Pearson's correlation coefficient as the metric for calculating pair-wise distances between variables as it has been previously recommended to maximize distances between spatial patterns of activity maps .…”

“…Correlation matrix-based k-means clustering. To group experimental contrasts with similar brain activity patterns, we applied an adapted meta-analytic k-means clustering procedure previously applied to large collections of experimental contrasts (Bottenhorn et al, 2019) (Fig. 1, step 3).…”

“…These metrics have previously been used in co-activation-based parcellation analyses to determine optimal clustering solutions (Bzdok et al, 2013b;Chang et al, 2013) for brain regions across taskindependent (Bottenhorn et al, 2019;Kahnt et al, 2012;Kelly et al, 2010) and task-dependent Chase et al, 2015a;Clos et al, 2013;Ray et al, 2015) brain states. We compared and contrasted the outcomes from viable clustering solutions and ultimately selected the solution identifying the greatest number of neurocognitively plausible MAGs for presentation in the main text.…”

“…Given that reward processing studies utilize a variety of complex tasks, task modeling approaches, and theoretical frameworks, it has been difficult to synthesize a quantitative summary of consistent associations between cognitive constructs and brain function. Emerging metaanalytic techniques have proven helpful in synthesizing neuroimaging findings across disperse experimental sites and diverse scientific approaches (Bottenhorn et al, 2019;Bzdok et al, 2015;Bzdok et al, 2013a;Bzdok et al, 2013b;Clos et al, 2013;Eickhoff et al, 2016;Laird et al, 2015;Ray et al, 2015;Riedel et al, 2018). Utilizing these techniques, we aimed to elucidate brain regions, and in turn networks, consistently linked with different aspects of reward processing.…”